coli strains that cause cystitis The BLAST nucleotide algorithm (blastn) showed that pRS218 is 99% identical to plasmids pUTI89 [GenBank:CP000244], p1ESCUM [GenBank:CU928148] and pEC14_114 [GenBank:GQ398086] of E. coli causing acute cystitis, pUM146 [GenBank:CP002168] of a strain of E. coli associated with Crohn’s disease,

and pECSF1[GenBank:AP009379] of an E. coli strain belonging to the phylogenetic group B2 which was isolated from feces of a healthy adult (Figure 2) [23]. Analysis of the repA1 sequence of FIIA replicon ACP-196 price of 24 IncFIB/IIA plasmids in pathogenic E. coli revealed three main lineages of virulence plasmids (Figure 3). All NMEC virulence plasmids were clustered into one lineage based on the repA1 sequence suggesting a common origin. Interestingly, pRS218 showed an identical origin with several virulence plasmids of E. coli causing cystitis (pUTI89 and pEC14_114), pECSF1 of the commensal MI-503 phylogenetic group B2 E. coli strain SE15 and pCE10A of NMEC strain CE10. Figure 2 Comparison of pRS218 sequence

to some virulence plasmids of other E. coli . Each code indicates a plasmid sequence. From top to bottom; pRS218, pUTI89 (a plasmid of the acute cystitis causing E. coli strain UTI89), pEC14_114 (a plasmid of

the uropathogenic E. coli strain EC14), pUM146 (a plasmid of the adherent invasive E. coli strain UM146), p1ESCUM (a plasmid of the acute cystitis causing E. coli strain UMN026) and pECSF1 (a plasmid of the commensal E. coli strain SE15). Each color box indicates clusters of ortholog genes present in plasmid sequences. White spaces in the blocks indicate the sequences that are not present in other plasmid sequences. Figure 3 Evolutionary relationship of IncFIB/IIA plasmids in pathogenic E. coli based on the repA1 sequence. The percentage of replicate trees in which the associated taxa diglyceride clustered together in the bootstrap test (500 replicates) is shown next to the branches. Genes of pRS218 are overly represented in NMEC strains compared to fecal E. coli Plasmid profiling revealed 27 of 53 (51%) of NMEC strains examined in the study harbored a plasmid similar in size to pRS218 (130-100 kb) (Table 2). Furthermore, PCR analysis revealed that a vast majority of pRS218-associated genes tested (n = 59) were overly represented (n = 52) among NMEC strains as compared to commensal E. coli (Table 3). Table 2 O serogroups of neonatal meningitis causing E.

The transition zone and basal bodies are further described here from the distal end toward the proximal end. The central space within the proximal half of the transition BGB324 supplier zone contained three distinct elements: faint spokes (denoted as ‘a’), an

outer concentric ring positioned just inside the microtubular doublets (denoted as ‘b’), and electron dense globules (denoted as ‘c’) (Figures 6D, 6L). Each faint spoke extended from a microtubular doublet toward the center of the transition zone. The globules were positioned at the intersections of each faint spoke and the outer concentric ring (Figures 6D, 6L). In more proximal points along the transition zone, nine “”radial connectives”" extended from each doublet toward the flagellar membrane (Figures 6E-F), and an opaque core was present within the central space when observed in both longitudinal and transverse section (Figures 6A, 6F-G). The opaque core consisted of six distinct elements: nine spokes extending from each doublet (denoted as ‘a’), the outer concentric ring (denoted as ‘b’), nine electron dense globules associated with the outer concentric ring (denoted as ‘c’), a central electron dense hub (denoted as ‘d’), an inner concentric ring (denoted as ‘e’) and nine radial connectives extending from

each doublet to the flagellar membrane (denoted as ‘f’) (Figures 6F, 6M). The radial connectives disappeared just above the distal boundary of the basal body (Figures 6A, 6G), and the elements within the central space disappeared just BAY 57-1293 price below the distal boundary of the basal body (Figures 6A, 6H). The dorsal basal body

(DB) and ventral basal body (VB) anchored the dorsal flagellum (DF) and ventral flagellum (VF), respectively. Both basal bodies were approximately 1.6 μm long, arranged in parallel to each other, and possessed nine transitional fibers extending from each triplet towards the cell membrane (Figures 6A, 6H-I). Internal cartwheel elements were present within the most proximal ends of both basal bodies (Figures 6J, 7G). Flagellar Root System The flagellar root system is described here from the proximal boundary of the basal bodies toward the distal boundary of the basal Fenbendazole bodies as viewed from the anterior end of the cell (Figure 7). The DB and the VB were joined with a connecting fiber and associated with three microtubular roots: the dorsal root (DR), the intermediate root (IR) and the ventral root (VR) (Figures 7A-B). The VB, IR and VR were also associated with three fibrous roots: the right fiber (RF), the intermediate fiber (IF) and the left fiber (LF) (Figure 7B). The DR and IR were associated with two thin laminae: the dorsal lamina (DL) and the IR-associated lamina (IL), respectively (Figures 7A-D, 9B).

The diffusion length (l D) can be defined as (where D is the surface diffusion coefficient and τ is the residence time), and the D has a strong proportional dependency on the substrate temperature (D ∝ T sub). RG7204 solubility dmso Then, driven by a high T sub, the l D can be significantly increased. In a thermodynamic equilibrium system, nanostructures tend to increase their dimensions by absorbing nearby adatoms to lower the surface energy until reaching the equilibrium in order to keep the energy of the whole system in the lowest state. Therefore, when more adatoms exist within the l D, the increased dimensions

of droplets can be expected. In terms of the uniformity, the color pattern of the FFT power spectrum represents the frequency of the height with a directionality. The FFT spectrum with the 2-nm DA in Figure 3a-1 showed a round shape due to the round shape of the droplets. With the 3-nm DA, a smaller core of the FFT pattern was observed due to the reduced height frequency associated with the reduced density in Figure 3b-1 as well as the AFM image in Figure 2b. Then, the FFT patterns in Figure 3c-1,d-1,e-1,f-1 with the increased DAs became smaller and smaller as the frequency of the height became narrower and uniform. In addition, flat tops of droplets were observed

with the line profiles of the DAs of 9 and 12 nm in Figures 3e,f and 5e,f. This is in strong contrast with the selleck round dome-shaped droplets at lower Molecular motor DAs. In the case of Si with the increased Au deposition amount, lateral growth of Au nanostructures occurred even with as low as approximately 5-nm DA and finally resulted in the formation of a merged Au layer at approximately 20-nm DA [45]. However, in

this experiment, the droplets were still maintained even above 12-nm DA (not shown here). Although it is not very logical to compare GaAs and Si directly due to the different growth conditions such as temperature, from this result, it can be expected that the binding energy between Au adatoms and surface atoms (E i) is weaker on GaAs surfaces than on Si (111). In other words, with increased DAs, droplets with lateral dimension expansion (coalescence) would require much higher DAs. In terms of the surface roughness (R q) during the DA variation from 2 to 3 nm, the R q was increased from 6.22 to 11.63 nm along with the expansion of the droplet dimensions as shown in Figure 4d. With the gradually increased DAs, the R q in Figure 4d showed an increasing trend accompanied with increased droplet dimensions, 6.22 nm for the 2-nm DA and 11.63 for the 3-nm DA, and gradually increased to 24.37 nm at the 9-nm DA. Then, the R q was saturated and showed a decreasing trend from there, likely due to the dominance of density decrease over the dimensional increase. Figure 6 shows the EDS spectra of the surface elemental characterization and the related SEM images of 4- and 12-nm samples. Generally, the resulting EDS spectra showed similar spectra for Ga and As with 4- and 12-nm DA as expected.

Expired gas composition and temperature, HR, ambient

temperature and humidity during whole TT were monitored using Cortex MetaMax® 3B System and Polar 725 heart rate monitor. Carbohydrate (CHO) and fat utilization was calculated based on the equation built in the software by selecting an assumed 15% total energy expenditure derived from protein. INCB018424 in vitro The rating of perceived exertion (RPE) using the 6-20 Borg scale was surveyed at 20-min intervals throughout the test. The pre- and post-testing body mass (BM) with removal of their racing suit was checked using an electronic BM scale. Urine sample was collected during 10-min relax time of the performance test for volume determination. To ensure subjects were enthusiastic about the test and performed at their highest level, they were informed at the beginning of the test that a prize would be awarded to the winner cycling the longest distance

during TT. Blood samples collection and biochemical measurements Venous blood was collected from anticubital arm vein into vacutainer tubes before the performance tests. Heparin plasma and serum were obtained after centrifugation at 3000 × g for 10 min. Samples were stored at -80°C until analyses. Finger blood was obtained via puncture for glucose determination at 0, 60, 125 and 155 min during the test. Free fatty acid (FFA), pyruvic acid (PA), and total antioxidant capacity (TAOC) in plasma were determined using commercial kits Atezolizumab supplier (Randox Laboratories Ltd, Crumlin, UK), and an auto-biochemical Dabrafenib analyzer (Hitachi, Tokyo, Japan). Plasma VE, malondialdehyde (MDA) and arginine levels, xanthine oxidase (XOD) and glutathione peroxidase (GPx) and superoxide dismutase (SOD) and creatine kinase (CK) activities, and blood urea nitrogen (BUN) and nitric oxide (NO) were measured using spectrophotometric kits (Jiancheng Bioengineering Institute, Nanjing, China). Serum insulin (Ins) and

cortisol (Cor) concentrations were measured using radioimmunoassay kit (Jiuding Diagnostic, Tianjin, China). Blood glucose (BG) was determined using handheld blood glucose analyzer (One Touch, LifeScan, Inc. Milpitas, CA). Diet and dietary record All subjects lived in a winter training camp and dined in the same canteen throughout the study, and were advised by a registered dietician to follow a diet with 60% total calories from CHO, 15% from protein, and 25% from fat for 2 days before each performance test. Generally subjects had a typical Chinese breakfast consisting of one chicken egg, two servings of steamed breads or noodles, deep-fried dough sticks, rice congee, bean milk, some meat, some vegetables and appetizers, and lunch and dinner consisting of meat, steamed rice, steamed breads, noodles, soup, milk, fruit and vegetables, etc. To assess dietary intake throughout the study, a 2-day food record was conducted at week 2, 4, 8, and 10.

N Engl J Med 2005, 353: 2012–2024.CrossRefPubMed 16. Barber TD, Vogelstein B, Kinzler KW: Somatic mutations of EGFR in

colorectal cancers and Glioblastomas. N Engl J Med 2004, 351: 2270–2883.CrossRef 17. Marie Y, Carpentier AF, Omuro AM: EGFR tyrosine kinase domain mutations in human gliomas. Neurology 2005, 64: 1444–1445.PubMed 18. Roberto B, Incheol S, Ritter ChristophA: Loss of PTEN/MMAC1/TEP in EGF receptor-expressing tumor cells counteracts the antitumor action of EGFR tyrosine kinase inhibitors. Oncogene 2003, 22: 2812–2822.CrossRef 19. Ingo K, Mellinghoff, Maria Y, Wang P: Molecular Determinants of the Response of Glioblastomas Volasertib solubility dmso to EGFR Kinase Inhibitors. N Engl J Med 2006, 354: 884–897. 20. Smith JustinS, Issei T, Sandra M: PTEN Mutation, EGFR Amplification, and Outcome in Patients With Anaplastic Astrocytoma and Glioblastoma Multiforme. J Natl Cancer Inst 2001, 93: 1246–1256.CrossRefPubMed 21. Harima Y, Sawada S, Nagata K: Mutation of the PTEN gene

in advanced cervical cancer correlated with tumor progression and poor outcome after radiotherapy. Int J Oncol 2001, 18: 493–497.PubMed 22. Endoh H, Yatabe Y, Kosaka T: PTEN and PIK3CA expression is associated with prolonged survival after gefitinib treatment AP24534 supplier in EGFR-mutated lung cancer patients. J Thorac Oncol 2006, 1: 629–634.CrossRefPubMed 23. Baselga J, Arteaga CL: Critical update and emerging trends in epidermal growth factor receptor targeting in cancer. J Clin Oncol 2005, 23: 2445–2259.CrossRefPubMed Thymidine kinase 24. Russell Sambrook: olecular Cloning. Third edition. America: CSHL Press;

2000:1235–1262. 25. Fan Z, Masui H, Altas I: Blockade of epidermal growth factor receptor function by bivalent and monovalent fragments of 225 anti-epidermal growth factor receptor monoclonal antibodies. Cancer Res 1993, 53: 4322–4328.PubMed 26. Fan Z, Lu Y, Wu X: Antibody-induced epidermal growth factor receptor dimerization mediates inhibition of autocrine proliferation of A431 squamous carcinoma cells. J Biol Chem 1994, 269: 27595–27602.PubMed 27. Prakash C, Shyhmin H, Geetha V: Mechanisms of Enhanced Radiation Response following EpidermalGrowth Factor Receptor Signaling Inhibition by Erlotinib (Tarceva). Cancer Res 2005, 65: 3328–3335. 28. Byeong HC, Chang GK, Yoongho L: Curcumin down-regulates the multidrug-resistance mdr1b gene by inhibiting the PI3K/Akt pathway. Cancer Letters 2008, 259: 111–118.CrossRef 29. Ivanco I, Sawyers CL: The phosphatidylinositol 3-kinase AKT pathway in human cancer. Nat Rev Cancer 2002, 2: 489–501.CrossRef 30. Liu W, James CD, Frederick L: PTEN/MMAC1 mutations and EGFR amplification in glioblastomas. Cancer Res 1997, 57: 5254–5257.PubMed 31. Yakut T, Gutenberg A, Bekar A: Correlation of chromosomal imbalances by comparative genomic hybridization and expression of EGFR, PTEN, p53, and MIB-1 in diffuse gliomas. Oncol Rep 2007, 17: 1037–1043.PubMed 32.

CrossRefPubMed Sotrastaurin nmr 29. Bischof DF, Janis C, Vilei EM, Bertoni G, Frey J: Cytotoxicity of Mycoplasma mycoides subsp. mycoides small colony type to bovine epithelial cells. Infect Immun 2008, 76:263–269.CrossRefPubMed 30. Zheng L, Roeder RG, Luo Y: S phase activation of the histone H2B promoter by OCA-S, a coactivator complex that contains GAPDH as a key component. Cell 2003, 114:255–266.CrossRefPubMed 31. Hara MR, Agrawal N, Kim SF, Cascio MB, Fujimuro M, Ozeki Y, Takahashi M, Cheah JH, Tankou SK, Hester LD, et al.: S-nitrosylated GAPDH initiates apoptotic cell death by nuclear translocation following Siah1 binding. Nat Cell Biol 2005, 7:665–674.CrossRefPubMed 32. Rawadi G, Roman-Roman S: Mycoplasma membrane lipoproteins induce proinflammatory

cytokines by a mechanism distinct from that of lipopolysaccharide. Infect Immun 1996, 64:637–643.PubMed 33. Pilo P, Martig S, Frey J, Vilei EM: Antigenic and genetic characterisation of lipoprotein LppC from Mycoplasma mycoides subsp. mycoides SC. Vet Res 2003, 34:761–775.CrossRefPubMed 34. Bonvin-Klotz L, Vilei EM, Kühni-Boghenbor K, Kapp N, Frey J, Stoffel MH: Domain check details analysis of lipoprotein LppQ in Mycoplasma mycoides subsp. mycoides SC. Antonie Van Leeuwenhoek 2008, 93:175–183.CrossRefPubMed 35. Bischof DF, Vilei EM, Frey J: Genomic differences between type strain PG1 and field strains of Mycoplasma mycoides subsp. mycoides small-colony type. Genomics 2006, 88:633–641.CrossRefPubMed

36. Gaurivaud P, Persson A, Le Grand D, Westberg J, Solsona M, Johansson KE, Poumarat F: Variability of a glucose phosphotransferase system permease in Mycoplasma mycoides subsp. mycoides Small

Colony. Microbiology 2004, 150:4009–4022.CrossRefPubMed 37. Jores J, Nkando I, Sterner-Kock A, Haider W, Poole J, Unger H, Muriuki C, Wesonga H, Taracha EL: Assessment of in vitro interferon-γ responses from peripheral blood mononuclear cells of cattle infected with Mycoplasma mycoides ssp. mycoides small colony type. Vet Immunol Immunopathol 2008, 124:192–197.CrossRefPubMed 38. Matthews LJ, Davis R, Smith GP: Immunogenically fit subunit Niclosamide vaccine components via epitope discovery from natural peptide libraries. J Immunol 2002, 169:837–846.PubMed 39. Janis C, Bischof D, Gourgues G, Frey J, Blanchard A, Sirand-Pugnet P: Unmarked insertional mutagenesis in the bovine pathogen Mycoplasma mycoides subsp. mycoides SC: characterization of a lppQ mutant. Microbiology 2008, 154:2427–2436.CrossRefPubMed 40. Poonia B, Sharma AK: Modulation of lympho-proliferative responses of ovine peripheral blood mononuclear cells by Mycoplasma mycoides ssp. mycoides (LC type). Vet Immunol Immunopathol 1998, 64:323–335.CrossRefPubMed 41. Smith GP, Petrenko VA, Matthews LJ: Cross-linked filamentous phage as an affinity matrix. J Immunol Methods 1998, 215:151–161.CrossRefPubMed 42. Gupta S, Arora K, Sampath A, Khurana S, Singh SS, Gupta A, Chaudhary VK: Simplified gene-fragment phage display system for epitope mapping.

Our data found Nanog mRNA had the highest specificity

in lung cancer. We further confirmed the high diagnostic value of Nanog protein levels by IHC, Nanog was overexpressed in lung cancer tissues, but rarely expressed in non-malignant lung tissue. Taken together, these results demonstrate that Nanog mRNA is a potential diagnostic marker for lung cancer. Nanog is a transcription factor that plays an important role in maintaining self-renewal of embryonic stem cells. Current studies have reported that the expression of Nanog was higher in multiple cancerous tissues than in their normal counterparts, including breast cancer [20], gastric adenocarcinomas [21], colorectal cancer [22], gliomas [23] and ovarian serous cystadenocarcinomas [24]. In this study, we found the expression of Nanog Palbociclib price mRNA in bronchoscopic biopsies of lung cancer patients was significantly higher compared to that in non-cancer patients. Although Nirasawa et al. [16] have also reported that the expression of Nanog mRNA was higher in surgically resected lung cancer tissues than in non-cancerous tissues, it is not known what cells express Nanog in non-cancerous lung

tissues. Using IHC, we found Nanog was only expressed in metaplastic AZD6244 squamous bronchial epithelium cells in 2 out of 50 non-malignant lung tissues, and was negative in normal airway epithelia. Therefore, Nanog may be a good diagnostic marker for lung cancer. In this study, our results showed that the mRNA levels of Bmi1, CD44 and CD133 were not

significantly different between lung cancer and non-malignant lung tissues. Further analyzed by IHC, we observed that Bmi1, CD44 and CD133 were not only expressed in lung cancers, Bmi1 and CD44 were also abundantly expressed in lung interstitial cells, inflammatory cells and bronchial epithelium cells, and CD133 was diffusely expressed in some normal bronchial epithelium cells and bronchial smooth muscle cells, consistent Thiamet G with previous studies [11, 25, 26]. Hence, Bmi1, CD44 and CD133 are poor diagnostic markers for lung cancer. Likewise, although the expression levels of Sox2 and Msi2 mRNA in lung cancer tissues were significantly higher as compared with non-malignant tissues, we found more than 80% of bronchoscopic biopsy specimens of non-cancer patients were positive for Sox2 and Msi2 mRNA, and all non-malignant tissues were positive for Sox2 and Msi2 protein expression, consistent with previous findings [10, 27, 28]. Therefore, Sox2 and Msi2 have poor diagnostic specificity in lung cancer. It is still controversial whether lung cancer cells express OCT4.

L-1) used to neutralise a solution of m CHI (g) of chitosan in 0.1 mol.L-1 HCl. V 2 (L) is click here the volume of NaOH added until neutralisation of the ammonium ions from chitosan, and V 1 (L) is the volume of NaOH added to cause the neutralisation of HCl in excess. MMCHI is the molecular mass of glucosamine units (161 g.mol-1). The extent of protonation (EPpH) of chitosan can be calculated

from Equation 2: (2) where% NH2 is the amount of non-protonated amine groups estimated from Equation 1 considering that V 2 is equal to the added volume of base to neutralise the ammonium ions from chitosan at the pH of interest (4.0, 5.0 and 6.0). Zeta potential analyses were performed using a Brookhaven ZetaPALS instrument with a laser light wavelength of 660 nm (35-mW

red diode laser, Holtsville, NY, USA). Standard square acrylic cells with a volume of 4.5 mL were used. The zeta potential measurements were performed at (25.0°C ± 2°C) under the Smoluchowski approximation [30], and 100 runs (five measurements of 20 cycles) were chosen for a good reproducibility. Results Characterisation of ZnS quantum dots capped by chitosan UV–vis spectroscopy The UV–vis absorption spectra of the ZnS nanoparticles produced using chitosan as the stabilising ligand (ZnS-chitosan nanoconjugates) are shown learn more in Figure 1A. The curves exhibit a broad absorption band between 250 and 360 nm associated with the first excitonic transition indicating that ZnS nanocrystals were synthesised within the ‘quantum confinement regime’ [31] at different pH to form colloidal suspensions capped by carbohydrate-based ligands (after 24 h). The band gap of quantum dots may be assessed Sclareol by theoretical, semi-empirical and empirical models. In this study, the optical band gap energy (E QD) was assessed from absorption coefficient data as a function of wavelength using the ‘Tauc relation’ [32]. This procedure allows to estimate the dimensions of nanoparticles in diluted colloidal suspensions in situ once the average

size of the ZnS nanocrystals can be estimated using the empirical model published in the literature [33, 34], which relates the nanoparticle size (r) to the E QD from a UV–vis spectrum (Equation 3): (3) Figure 1 UV–vis spectroscopy analysis. (A) Spectra of ZnS-chitosan conjugates synthesised at different pH. (B) Optical band gap using the Tauc relation of ZnS-chitosan conjugates synthesised at different pH. (a) pH = 4.0, (b) pH = 5.0, (c) pH = 6.0. Inset: analysis of the effect of pH during the synthesis on the average ZnS quantum dot size (2r) and respective band gap energy (E QD). The E QD values extracted from the curves using the Tauc relation (Figure 1B) were equal to 3.74 ± 0.02, 3.79 ± 0.02 and 3.92 ± 0.02 eV for pH = 4.0, 5.0 and 6.0, respectively. These band gap values are higher than the reference bulk value of 3.54 to 3.

pseudomallei mouse monoclonal and a secondary anti-mouse/Alexa488 antibody. GDC-0973 concentration Scale bar: 90 μm. (B) Visual representation of the MNGC Image Analysis procedure. Each object (Nuclei) is pseudocolored with a unique color in the nucleus segmentation panel. Bacterial spots are pseudocolored in green in the spot segmentation panel. Nuclei clustering: Nuclei are clustered based on distance as described in Experimental procedures to generate the Cluster population. In the MNGC selection panel, image objects classified as MNGC are pseudocolored in green, and non-MNGC objects are pseudocolored in red. (C) Histograms representing the quantification of cellular attributes of the

cluster population as measured by the MNGC image analysis procedure described in Figure  1B. (D) Histograms showing the results of the quantification of cellular attributes related to bacterial spot formation. In C and D means +/- standard deviation (SD) are

shown for three independent B. pseudomallei macrophage infections performed on separate days and with six replicates/plate. n = 18 and > 500 nuclei were analyzed per well. **** p < 0.0001. As observed in the fluorescence microscopy images, Bp infection induced cell-to-cell fusion, clustering of the nuclei and cell body enlargement in a substantial fraction of infected macrophages when compared to mock infected samples (Figure  1A). These cellular objects click here fit the definition of MNGC. A large number of Bp bacterial spots were found to be

either internalized or in close proximity with the boundaries of infected cell bodies. In these experimental conditions not all the infected cells appear to be part of an MNGC object (Figure  1A). Hence, it was important to develop an HCI analysis that would recognize and distinguish MNGC objects from non-MNGC objects in a heterogeneous population of infected cells. To address this issue, we took advantage of the close proximity of the nuclei in MNGC’s to recognize and classify Astemizole MNGC clusters. Briefly, and as shown in Figure  1B, cell nuclei were first identified by using the Hoechst 33342 channel image, thus obtaining a population of objects that was named “Nuclei”. The cell body edges were identified by expanding the body of the nucleus detected in the previous step. The cell body borders were then detected by using the CellMask DeepRed channel image. Bp spots were identified using the Bp antibody channel image. Several cellular attributes were calculated for the Nuclei population, the most relevant being: number of objects, cell body area and number of bacterial spots per object. The next step in the image analysis consisted in recursively clustering distinct Nuclei objects together into a single “Cluster” object, provided that their nuclei were either touching or adjacent.

A team of authors from three universities which have been among the

leaders in ESD (Polytechnic University of Catalunya, Spain; Delft University of Technology, Netherlands; and Chalmers University of Technology, Sweden) describe their progress in bringing ESD into the Bachelors level programs at these universities. The articles in this special feature issue have several important commonalities. Since many of these initiatives have been established only recently, it has not always been possible to offer an in-depth assessment of the successes (or lack thereof) for a particular approach. These articles should, therefore, be viewed as ‘case reports’ on ESD initiatives underway which, we hope, will suggest and stimulate additional R428 price initiatives at other universities. There is a clear common theme to all of these initiatives, though, and that is the inter- or trans-disciplinary nature of the programs

and curricula being developed and implemented. As Yoshikawa (2008) has noted, this aspect, which he terms ‘synthesiology,’ is a core element of sustainability science. Wilson (1998) has similarly designated ‘consilience,’ defined as the unity of knowledge, or the synthesis of knowledge from different specialized fields of human endeavor, as Selleck Fulvestrant being essential for addressing the problems that face human society and the natural environment. Professor Akito Arima’s message, “A Plea for More Education for Sustainable Development,” clearly states both the need for and the difficulties associated with this approach. The articles in this Special Feature Issue highlight some of the Anacetrapib many strategies that are being developed to introduce these principles into higher education. If these efforts succeed, we may be at the threshold

of a paradigm shift in our educational systems, which could be as far-reaching and momentous as the transition which took place in the 15th–16th centuries, from the medieval scholastic system to the empirical, discipline-based educational model which still forms the basis of our universities. This model has served us very well in the past, leading to enormous expansions of human knowledge, technology, and the global economy, but it may not be sufficient to address the problems of global sustainability that we now face, which result, in part, from this growth in human activity. Indeed, this transition must succeed if we are to leave a healthy environment, a just society, and a sustainable future to our descendants. References Wilson EO (1998) Consilience: the unity of knowledge. Alfred A. Knopf/Random House, New York Yoshikawa H (2008) Synthesiology as sustainability science. Sustain Sci 3(2):169–170CrossRef”
“Introduction Most of the problems arising from the impact of human activities on the Earth’s life support systems come from complex, global, and social human interactions. Unless we understand these interactions, we will not be able to design a path towards sustainable development.